Machine learning-based methods and systems for modeling user-specific, activity specific engagement predicting scores

ABSTRACT

A machine-learning based method includes receiving an instruction to model an engagement predicting score for a user. User-specific, activity-specific data is obtained from digital resources that include a user-specific activity performance data regarding performance of at least one activity by the user, an object data for an object that allows the user to perform the at least one activity, and user-specific personal data of the user. A user-specific activity engagement labeling data for the at least one activity is predicted by utilizing a first-type data pipeline on the at least one user-specific activity performance data. User-specific, activity-specific data features are predicted by utilizing a second-type data pipeline on the user-specific, activity-specific data. The engagement predicting score is predicted from the user-specific, activity-specific data features and the user-specific activity engagement labeling data. A computing device is instructed to present at least one user-specific activity-related action instruction.

FIELD OF TECHNOLOGY

The present disclosure generally relates to improved machinelearning-based systems, and more specifically to machine learning basedmethods and systems for modeling user-specific, activity-specificengagement predicting scores.

BACKGROUND OF TECHNOLOGY

A computer network system may include a group of computers (e.g.,clients, servers, smart routers) and other computing hardware devicesthat are linked together through one or more communication channels tofacilitate communication and/or resource-sharing, via one or morespecifically programmed graphical user interfaces (GUIs) of the presentdisclosure, among a wide range of users.

SUMMARY OF DESCRIBED SUBJECT MATTER

In some embodiments, the present disclosure provides an exemplarytechnically improved computer-based method that includes receiving, by amachine-learning processor, an instruction to model at least oneuser-specific activity-specific engagement predicting score for at leastone user from a plurality of users. User-specific, activity-specificdata may be obtained, by the machine-learning processor, from aplurality of digital resources, based on the instruction, where theuser-specific, activity-specific data may include: (i) at least oneuser-specific activity performance data regarding performance of atleast one activity by the at least one user, (ii) at least one objectdata for at least one object that allows the at least one user toperform the at least one activity, and (iii) at least one user-specificpersonal data of the at least one user. A user-specific activityengagement labeling data for the at least one activity may be predicted,by the machine-learning processor, by utilizing a first-type datapipeline on the at least one user-specific activity performance data. Aplurality of user-specific, activity-specific data features may bepredicted, by the machine-learning processor, by utilizing a second-typedata pipeline on the user-specific, activity-specific data. The at leastone user-specific activity-specific engagement predicting score may bepredicted, by the machine-learning processor, based on at least onemachine-learning model, by utilizing: (i) the user-specific activityengagement labeling data for the at least one activity, and (ii) theplurality of user-specific, activity-specific data features. At leastone computing device may be instructed, by the machine-learningprocessor, based on the at least one user-specific activity-specificengagement predicting score, to present at least one user-specificactivity-related action instruction that predicts at least oneuser-specific activity-related action to be performed with at least oneuser.

In some embodiments, the present disclosure provides an exemplarytechnically improved computer-based system that includes at least thefollowing components a memory and a machine-learning processor whichexecutes computer code that causes the machine-learning processor toreceive an instruction to model at least one user-specificactivity-specific engagement predicting score for at least one user froma plurality of users, to obtain from a plurality of digital resources,based on the instruction, user-specific, activity-specific data, wherethe user-specific, activity-specific data may include (i) at least oneuser-specific activity performance data regarding performance of atleast one activity by the at least one user, (ii) at least one objectdata for at least one object that allows the at least one user toperform the at least one activity, and (iii) at least one user-specificpersonal data of the at least one user, to predict a user-specificactivity engagement labeling data for the at least one activity byutilizing a first-type data pipeline on the at least one user-specificactivity performance data, to predict a plurality of user-specific,activity-specific data features by utilizing a second-type data pipelineon the user-specific, activity-specific data, to predict based on atleast one machine-learning model, the at least one user-specificactivity-specific engagement predicting score, by utilizing (ii) theuser-specific activity engagement labeling data for the at least oneactivity, and (ii) the plurality of user-specific, activity-specificdata features, and to instruct based on the at least one user-specificactivity-specific engagement predicting score, at least one computingdevice to present at least one user-specific activity-related actioninstruction that predicts at least one user-specific activity-relatedaction to be performed with at least one user.

BRIEF DESCRIPTION OF THE DRAWINGS

Various embodiments of the present disclosure can be further explainedwith reference to the attached drawings, wherein like structures arereferred to by like numerals throughout the several views. The drawingsshown are not necessarily to scale, with emphasis instead generallybeing placed upon illustrating the principles of the present disclosure.Therefore, specific structural and functional details disclosed hereinare not to be interpreted as limiting, but merely as a representativebasis for teaching one skilled in the art to variously employ one ormore illustrative embodiments.

FIG. 1 is a block diagram of a system for modeling user-specific,activity-specific engagement predicting scores in accordance with one ormore embodiments of the present disclosure;

FIG. 2 is a flow diagram for modeling user-specific, activity-specificengagement predicting scores in accordance with one or more embodimentsof the present disclosure;

FIG. 3 schematically illustrates a set of data attributes of a user fromaggregated user-specific data in accordance with one or more embodimentsof the present disclosure;

FIG. 4 is a flow diagram of a time-series extraction process datapipeline in accordance with one or more embodiments of the presentdisclosure;

FIG. 5 is a flow diagram of a feature extraction data pipeline inaccordance with one or more embodiments of the present disclosure;

FIG. 6 is an exemplary histogram illustrating a total amount ofportfolio assets split into asset classes versus each asset class inaccordance with one or more embodiments of the present disclosure.

FIG. 7 is an exemplary histogram illustrating a total amount ofportfolio assets split into sectors versus each sector in accordancewith one or more embodiments of the present disclosure.

FIG. 8 is a flow diagram for classifying assets based on a number ofattributes in accordance with one or more embodiments of the presentdisclosure;

FIG. 9 is a flow diagram of a label creation data pipeline in accordancewith one or more embodiments of the present disclosure;

FIG. 10 is a flow diagram of a training data pipeline in accordance withone or more embodiments of the present disclosure;

FIG. 11 is a flow diagram of an incremental learning inference datapipeline in accordance with one or more embodiments of the presentdisclosure;

FIG. 12 is a first graph illustrating computational results inaccordance with one or more embodiments of the present disclosure;

FIG. 13 is a second graph illustrating computational results inaccordance with one or more embodiments of the present disclosure;

FIG. 14 is a third graph illustrating computational results inaccordance with one or more embodiments of the present disclosure;

FIG. 15 is a graph illustrating a potential usage of a line of credit inaccordance with one or more embodiments of the present disclosure;

FIG. 16 is an exemplary output on a graphical user interface inaccordance with one or more embodiments of the present disclosure;

FIG. 17 is a flow diagram of a pull single lead scoring in accordancewith one or more embodiments of the present disclosure;

FIG. 18 is a flow diagram for scoring a batch process of leads inaccordance with one or more embodiments of the present disclosure;

FIG. 19 is a flowchart of an exemplary method for modelinguser-specific, activity-specific engagement predicting scores inaccordance with one or more embodiments of the present disclosure;

FIG. 20 depicts a block diagram of an exemplary computer-basedsystem/platform in accordance with one or more embodiments of thepresent disclosure;

FIG. 21 depicts a block diagram of another exemplary computer-basedsystem/platform in accordance with one or more embodiments of thepresent disclosure; and

FIGS. 22 and 23 are diagrams illustrating implementations of cloudcomputing architecture/aspects with respect to which the disclosedtechnology may be specifically configured to operate, in accordance withone or more embodiments of the present disclosure.

DETAILED DESCRIPTION

Various detailed embodiments of the present disclosure, taken inconjunction with the accompanying figures, are disclosed herein;however, it is to be understood that the disclosed embodiments aremerely illustrative. In addition, each of the examples given inconnection with the various embodiments of the present disclosure isintended to be illustrative, and not restrictive.

Throughout the specification, the following terms take the meaningsexplicitly associated herein, unless the context clearly dictatesotherwise. The phrases “in one embodiment” and “in some embodiments” asused herein do not necessarily refer to the same embodiment(s), thoughit may. Furthermore, the phrases “in another embodiment” and “in someother embodiments” as used herein do not necessarily refer to adifferent embodiment, although it may. Thus, as described below, variousembodiments may be readily combined, without departing from the scope orspirit of the present disclosure.

In addition, the term “based on” is not exclusive and allows for beingbased on additional factors not described, unless the context clearlydictates otherwise. In addition, throughout the specification, themeaning of “a,” “an,” and “the” include plural references. The meaningof “in” includes “in” and “on.”

It is understood that at least one aspect/functionality of variousembodiments described herein can be performed in real-time and/ordynamically. As used herein, the term “real-time” is directed to anevent/action that can occur instantaneously or almost instantaneously intime when another event/action has occurred. For example, the “real-timeprocessing,” “real-time computation,” and “real-time execution” allpertain to the performance of a computation during the actual time thatthe related physical process (e.g., a user interacting with anapplication on a mobile device) occurs, in order that results of thecomputation can be used in guiding the physical process.

As used herein, the term “dynamically” and term “automatically,” andtheir logical and/or linguistic relatives and/or derivatives, mean thatcertain events and/or actions can be triggered and/or occur without anyhuman intervention. In some embodiments, events and/or actions inaccordance with the present disclosure can be in real-time and/or basedon a predetermined periodicity of at least one of: nanosecond, severalnanoseconds, millisecond, several milliseconds, second, several seconds,minute, several minutes, hourly, several hours, daily, several days,weekly, monthly, etc.

As used herein, the term “runtime” corresponds to any behavior that isdynamically determined during an execution of a software application orat least a portion of software application.

Embodiments of the present disclosure herein disclose systems andmethods for increasing yields from securities-based lending (SBL)products issued by an entity. At least one machine learning model (MLM)may be trained to predict via a utilization prediction score for each ofa plurality of potential borrowers having a higher likelihood ofgenerating high yields through the analysis of previous borrowingpatterns. The at least one MLM may be configured to recommend actions tobe taken by investment advisors to maximize yields from the portfoliosof the potential borrowers targeted from a plurality of customers. Theat least one MLM may be configured to suggest drawdown and/or paybackactions aimed at current borrowers to minimize tax expenditure, bankingfees and other taxes/commissions. Stated differently, the at least oneMLM is configured to predict, for a given loan/line of credit (e.g.,SBL), the probability that the line of credit will be used by a borrowerin a predefined time interval after the line of credit is opened, suchas a year, for example.

A plurality of user-specific data objects may be associated with aplurality of users and managed by a server associated with an entity.The plurality of user-specific data objects may include a plurality ofuser-specific activity performance data (e.g., debts/loans/lines ofcredit), a plurality of object data (e.g., assets/financial accounts)that allows the user to perform the at least one activity (e.g., sincethe asset and financial accounts provide collateral for the loan and/orline of credit), and/or personal data (e.g., PII, age, gender,demographic attributes, psychographic attributes, and/or behavioralattributes).

Optionally and/or alternatively, data may be stored may be stored indata records. Thus, the plurality of user-specific data objects maystore obligation-based data records (e.g., debts, loans, and/or lines ofcredit), asset-based data records (e.g., assets/financial accounts),and/or personal data records, for example as shown herein below in FIG.1 .

The entity server may transfer to a customer's account, an SBL line ofcredit to the customer's debt portfolio such that if used by thecustomer, the line of credit may generate yields and value for theentity. The entity may maintain a database of entity data records of thelines of credit given to any of the plurality of customers. Thelikelihood that a customer may use a line of credit based on thecustomer's asset portfolio, and debt portfolio as well as historicalactivity data in the debt portfolio may be modeled as a utilizationprediction score outputted by a machine learning model. The utilizationprediction score may also be referred to herein as a user-specificactivity specific engagement predicting score.

In some embodiments, the utilization prediction score may be indicativeof a first likelihood that the at least one user will use a line ofcredit generating revenue for the entity. In other embodiments, theutilization prediction score may be indicative of a second likelihoodthat the at least one user will churn, or close, the line of creditafter given to the at least one user by the entity, where churningreduces revenue for the entity. In yet other embodiments, theutilization prediction score may be based on both the first and secondlikelihoods.

In some embodiments, a user obtaining a line of credit from an entitysuch as a financial institution. The term churning as used herein mayrefer to the user closing the line of credit without having drawn-downon the line of credit. In other embodiments, the term churning as usedherein may refer to the user closing the line of credit havingdrawn-down on the line of credit. As churn reduces income yield to thelending entity, the entity may provide incentive(s) in order to reducechurn such as, for example, lower fees, lower interest rates or otherincentives to utilize the line of credit. In some embodiments featureswhich may impact the probability of churn include tenure (length of timesince the line of credit was established), market value trends (of theunderlying assets under management), cash trends (within the account)and the number of days since the most recent activity on the line ofcredit account.

FIG. 1 is a block diagram of a system 10 for modeling user-specific,activity-specific engagement predicting scores in accordance with one ormore embodiments of the present disclosure. System 10 may include aserver 15, P computing devices 90A and 90B where P is an integer, and/orM electronic resources 100A and 100B denoted ELECTRONIC RESOURCE1 . . .ELECTRONIC RESOURCEM all communicating 35 over a communication network.

In some embodiments, the server 15 may be associated by an entity or afinancial entity that may provide securities-based lending (SBL) linesof credit to users (e.g., customers) such as a user 80A and a user 80Bby an entity user (e.g., a financial advisor) such as an entity user 85Aand an entity user 85B. The entity user (e.g., the banker or financialadvisor) may perform the profiling analyses for at least one user usingthe system 10 to determine a likelihood that the at least one user mayuse the SBL line of credit offered by the financial institution andthus, generate yields for the financial institution when the at leastone user uses the SBL line of credit. The entity user, such as a banker,financial advisor and the like, may use the user-specific output datafrom system 10 to determine whether or not to offer the SBL line ofcredit to the at least one user (e.g., the at least one customer).

In some embodiments, an electronic resource or digital resource in thecontext used herein may refer to, but not limited to a resource in whicha user's financial and/or personal data may be stored in a plurality ofdata elements such as in a storage device of any bank and/or financialentity computing server. An electronic resource may also include, forexample, social media and/or other data repositories such as Facebook,Twitter, Google, Instagram, and/or LinkedIn, for example, and accessibleover the communication network 30 with user-specific data that may beuseful in determining both creditworthiness and/or SBL credit lineusage. The terms electronic resource and digital resource may be usedinterchangeably herein.

In some embodiments, the server 15 may include a machine-learningprocessor 20 for executing, in part, machine-learning and/or predictionalgorithms, input and/or output (I/O) devices 25, a communicationcircuitry 40 and a memory 45. The machine-learning processor 20 mayexecute software code in software modules for performing the functionsdescribed herein. The software modules may include a data aggregator 21,a time-series extraction pipeline 22, a features extraction pipeline 23,a machine learning model 24, a prediction outcome manager 26, and/or agraphic user interface (GUI) Manager 27.

In some embodiments, the memory 45 may store an entity server (ES)database 50 and/or a user-specific data object database 60. The ESdatabase 50 may include a plurality of Q ES data records where Q is aninteger denoted by ES DATA RECORD1 51 . . . ES DATA RECORDQ 52.

In some embodiments, the user-specific data object database 60 mayinclude a plurality of N data objects where N is an integer denoted byDATA OBJECT1/USER1 62 . . . DATA OBJECTN/USERN 70. Each data object inthe user-specific data object database 60 may be used for holding datarecords related to a unique user. For example, the DATA OBJECT1/USER1 62may include at least one obligation-based data record 64, at least oneasset-based data record 65, and/or at least one personal data record 67.The term portfolio 65 may be used synonymously with the at least oneasset-based data record 65 that may include all of USER1's assets, bankaccounts, securities holding, etc. Similarly, the DATA OBJECTN/USERN 70may include at least one obligation-based data record 71, at least oneasset-based data record 75, and/or at least one personal data record 73.A term portfolio 75 of USERN may be used synonymously with the at leastone asset-based data record 72 that may include all of USERN's assets,bank accounts, securities holding, etc.

In some embodiments, the user 80A may interact with the entity user 85A.The entity user 85A may enter personal details and/or financial detailsof the user 80A into a graphic user interface1 92A denoted GUI1 of thecomputing device 90A that is in communication 35 to transmit to and/orreceive data from the server 15. Similarly, the user 80B may interactwith the entity user 85B. The entity user 85B may enter personal detailsand/or financial details of the user 80B into a graphic user interface192B denoted GUIP of the computing device 90B that is in communication 35to transmit to and/or receive data from the server 15.

In some embodiments, the computing device 90A may include a processor191A, a memory 93A, a communication circuitry 94A for communicating 35over the communication network 30, and input and/or output (I/O) devices95A. The processor1 91A may receive instructions from GUI Manager 27 tocontrol the GUI1 92A via the communication network 30. Similarly, thecomputing device 90B may include a processorP 91B, a memory 93B, acommunication circuitry 94B for communicating 35 over the communicationnetwork 30, and input and/or output (I/O) devices 95B. The processorP91B may receive instructions from GUI Manager 27 to control the GUIP 92Bvia the communication network 30.

FIG. 2 is a flow diagram 110 for modeling user-specific,activity-specific engagement predicting scores in accordance with one ormore embodiments of the present disclosure. The flow diagram 110 mayrepresent a top-level flow diagram of the method for modelinguser-specific, activity-specific engagement predicting scores that mayinclude a gathering step S1 115, a creation and/or update step S2 120 ofa borrower's persona, a recommendation step S3 125 for recommending thenext-best action for getting the user to apply for an SBL line of creditand to use it, and a monitoring step S4 130 where yields generated fromthe user's use of the SBL line of credit may be monitored. Theutilization prediction score may be a loan-to-value (LTV) metricassigned to the user.

In some embodiments, in the gathering step S1 115, a series of datatransforms, aggregations, and filtering (i.e. an algorithm) may be usedto create a proprietary set of information that may include data queriedand structured in a specific way in order to provide information to auser or a system; also known as a “data feature” stored in data recordsin the electronic resources. This data may include (i) historicaluser-specific data that may be collected from any of the plurality ofelectronic resources 100A and 100B associated with financialinstitutions, (ii) data shared by the users (e.g. borrowers) aboutthemselves, their assets, and any other user-specific personal data,(iii) social media and other digital data related to the borrower'sdigital footprint from any of the plurality of electronic resources 100Aand 100B associated with social media sites and/or databases, and/or(iv) third-party data from any of the plurality of electronic resources100A and 100B associated with third-party data websites and/ordatabases.

In some embodiments, the data aggregator 21 may receive historical dataover the communication network 30 related to the borrower such ashistorical borrowing data may be used to generate data features that mayinclude, but are not limited to, the composition of each portfolio (andthe ratios of each asset in relation to every other asset), changes incomposition, transactions, currently pledged and non-pledged collateral,abandoned loan applications, drawdown and payback rates, market movementin underlying pledged and non-pledged positions for customers, duration,amount, credit worthiness as well as ratios, trends, averages, medians,correlations data element stored in the obligation-based data record ofa particular user (e.g., borrower).

In some embodiments, the data aggregator 21 may receive data shared byborrowers may include personal identification information (PII) that theborrower may share with a financial institution to get access toservices and products. Typical examples of such information are (i) Nameand Surname, (ii) Full Address, (iii) Social Security number, and/or(iv) date of birth.

In some embodiments, the data aggregator 21 may receive data from socialmedia and other data repositories. This may be data that the borrowerhas already shared on social media and on other digital repositories.Examples of such repositories may include Linkedin, Twitter, Facebook,Instagram as well as Demographic and psychographic consumer databases(e.g., Acxiom).

In some embodiments, the data aggregator 21 may receive third party datathat may include a broad range of institutional grade data, includingreal-time and historical stock prices, fundamentals, forex, and/orcryptocurrency. Moreover, a broad range of financial news may becollected and aggregated by third-party providers that may be a veryvaluable source of information to detect macro events that may influenceborrower's behaviors and driving yields.

In some embodiments, given a borrower's PII data point, all of the datasources stored in the plurality of electronic resources as describedabove may be scanned to associate data relevant to the target borrower.In particular, pattern matching, Natural Language processing (NLP),probabilistic analyses, or any combination thereof may be used toassociate the information found in the plurality of electronic resourcesto the PII data of the borrower.

FIG. 3 schematically illustrates a set of data attributes 140 of a user145 from aggregated user-specific data in accordance with one or moreembodiments of the present disclosure. The aggregated user-specific datamay be used to create and/or update the Borrower's persona as in step S2120 of FIG. 2 . The set of attributes 140 for the user 145 named RichardBryce, for example, may be defined at three levels: a product layer 150,a sales layer 160, and a borrower layer 170.

In some embodiments, the data attributes associated with the borrowerlayer 170 may include data features that may be identified wherecustomers who have pledged marketable securities against a non-purposeloan which may be labelled according to specific pre-defined customersegments. The data attributes may include PII Data 162, a borrowerprofile 164, an investment profile 166, a portfolio composition 168, anda market influence 172. This analysis may be used to seed machinelearning algorithms which may provide the basis to identify likelyborrower behavior across the broader SBL loan book that may also beapplied to the company's wider wealth client base to identifyprospective borrowers and their likely borrower behavior. In addition,the investment profile and portfolio composition may by captured in theborrower layer 170.

In some embodiments, the data attributes associated with the sales layer160 may include attributes that are instrumental to sales and marketingoperations. The data attributes may include a sales goal 146, a lifetimevalue 148, wealth 152, and a churn likelihood 154. In particular,attributes such as the lifetime value 148 and the churn likelihood 154may be used by salesforce-tailor marketing messages and may be used toprioritize communication with certain cohorts of borrowers.

In some embodiments, the data attributes of the product layer 150 mayinclude attributes such as utilization drivers 142 and a productrecommendation 144 that capture the interaction of the borrower withproducts or the suitability for a specific product. These attributes maybe evaluated simultaneously by running machine learning algorithms. Thetraining and test data may be used to generate machine learning modelsfor each attribute described above.

In some embodiments, the machine learning model 24 may be validatedagainst labelled customer data in order to determine the optimalalgorithm for a given set of customers. As new customers with marketablesecurities collateral are provided, the machine learning model 24 may bere-trained to provide an optimized algorithm by dynamically adjustingthe parameters of the features and the underlying code that generatesthe features each runtime.

In some embodiments, the user-specific data attributes from step S2 120of FIG. 2 may be inputted to the machine learning model 24. The machinelearning model 24 may output in the step S3 125 on the GUI1 92A . . .GUIP 92B, the recommended next-best-action that the entity user or thefinancial advisor may take to increase the likelihood that the user mayuse an SBL line of credit if offered. For example, once the borrower'sattributes are calculated, they can be used to suggest next actions.Such actions may be taken programmatically such as by the server 15automatically sending email messaging about the SBL line of credit, forexample, or by a sales representative calling a borrower likely to churnupon reading the outputted recommended next-best-action for the borroweron the GUI1 92A . . . GUIP 92B. When applied to multiple customers, ofparticular interest may be the ability to create multi-level,hyper-customized marketing campaigns targeting cohorts of borrowers thatare more likely to generate yields for the financial institution.

In some embodiments, the terms “features” and “attributes” may be usedinterchangeably herein.

In some embodiments, if the SBL line of credit may be underutilized, aprescriptive action may be to implement a marketing campaign to generatemovement and to prevent SBL churning. If the SBL line of credit may beoverutilized, a prescriptive action may be to alert the borroweralternative ways to get credit. If the SBL line of credit may beleveraged, a prescriptive action may be to alert the borrower. If theSBL line of credit may be non-optimized with different line movementpatterns, such as with Big-Small movements, for example, a prescriptiveaction may be to alert the borrower to suggest alternative ways to getcredit. The prescription actions may also be referred to asuser-specific, activity-related actions.

In some embodiments, the prediction outcome manager 26 may be used inStep S4 130 to monitor yields and to evaluate the impact of eachprescriptive action on borrower behavior that may be put in place basedon the machine learning models 24. Based on the monitoring results, theprescriptive actions on borrower behavior may be optimized.

In some embodiments, the machine learning model 24 may perform thefunctions of the prediction outcome manager 26.

FIG. 4 is a flow diagram of a time-series extraction process datapipeline 200 in accordance with one or more embodiments of the presentdisclosure. The time-series extraction process data pipeline 200 (e.g.,the time-series extraction pipeline 22 of FIG. 1 ) may be configured toextract outstanding loan balances for the plurality of users that may beimplemented in three steps: a data harvest step 205, a data analysisstep 210, and a data aggregation step 215. The time-series extractionprocess data pipeline 200 may be used to generate outstanding balancetime series and to collect user-specific information from the loans datapulled directly via an application programmable interface (API), forexample. The time-series extraction pipeline may also be referred toherein as a first-type data pipeline.

In some embodiments, the data harvest step 205 may include themachine-learning processor 20 pulling data from the plurality ofelectronic resources 100A and 100B in a [T01] APIs data harvest step 220via at least one API. The loan data 230 may be stored locally in theobligation-based data records 64 of the user-specific data objectdatabase 60 in a [T02] local data cache step 225. The loan data 230 mayinclude an obligator, an obligation, a credit policy ID, a CommitmentAmount Outstanding Balance that may further include a primaryBorrowerID,an internalContactID, an evaluationID, a CollateralAccountID, and alineOfBusiness ID.

In some embodiments, the data analysis step 210 may include themachine-learning processor 20 assessing the new data quality in a [T03]Data Quality assessment step 240 by both manual and/or automatedprocesses. In some embodiments, the machine-learning processor 20 mayexecute a [T04] New Loan Detection step 235 to detect new loans withrespect to the last time that data was pulled in step 235.

In some embodiments, the data aggregation step 215 may include anoutstanding balance time series (TS) creation [T05] step 245 in order toextract the history of each loan from our local data from the local datacache 225. In order to compute the outstanding value of a given loan forall days in a [T06] Timeseries filling step 250, the machine-learningprocessor 20 may fill missing values into many outstanding balance timeseries outputted from the time series (TS) creation [T05] step 245. Themachine-learning processor 20 may interpolate data within missing datatime intervals in the Timeseries filling step 250. This process may leadto two results for each loan: [O1] outstanding balances time series (TS)dataset for each loan [O1] 255 and [O2] a Loans Summary 260, or asummarized information about the loan itself [O2], which may include foreach loan, the committed amount [O2] and other descriptive informationsuch as, for example, a start and an end date for each loan.

In some embodiment, the system 10 may be configured to classifyborrowers through the distribution of their assets. To represent adistribution, the machine-learning processor 20 may use a frequencycount feature that may capture the portion of wealth allocated to aparticular asset or category of asset. The machine-learning processor 20may classify the portfolio composition by first considering twodifferent aspects: (i) market value, and (ii) haircuts applied byfinancial institutions while offering a loan.

FIG. 5 is a flow diagram of a feature extraction data pipeline 300 inaccordance with one or more embodiments of the present disclosure. Thefeature extraction data pipeline 300 (e.g., the feature extractionpipeline 23 of FIG. 1 ) may include a setup phase 305, a classificationphase 310, and a generation phase 315. The feature extraction datapipeline 300 may be used to generate portfolio features for training themachine learning model 24 using historical financial data of borrowers.The feature extraction pipeline may also be referred to herein as asecond-type data pipeline.

In some embodiments, the machine-learning processor 20 may execute thefeature extraction pipeline 23 (FIG. 1 ) or feature extraction datapipeline 300 of FIG. 5 . In the setup phase 305, the machine-learningprocessor 20 may use as an input the dataset of [O1] outstandingbalances time series dataset 320, a portfolio date/features type 325,and/or the historical financial data from the local data cache [T02] 335into a [P01] Assets Vocabulary Creation module 330. In otherembodiments, the machine-learning processor 20 may filter out from theborrower's financial data, all loans having less than 365 days ofhistorical data. The [P01] Assets Vocabulary Creation module 330 maycreate a dictionary of all possible asset IDs within the financial data.

In some embodiments, the machine-learning processor 20, in theclassification phase 310, may input an asset class name [S01] lookuptable 345 and a Committee on Uniform Security Identification Procedures(CUSIP) code [S02] lookup table 355 into an Asset Hierarchicalclassification [P02] module 310. These lookup tables respectivelydescribe the class and/or the sector that a given asset may belong towith three levels of granularity. This information may be attached toall loan data in the user-specific object database 60 so that each assetmay be described in a very detailed fashion [P02].

In some embodiments, the machine-learning processor 20, in thegeneration phase 315, using an aggregation module 360 may aggregateassets by feature_type [P03] so as to split each portfolio (in terms ofeither market value or top up amount) according to different types ofsegmentation. The machine-learning processor 20 may consider the amountof each asset of the vocabulary generated by module 330 (i.e., [P01]),For each class in asset class name [S01] lookup table 345, themachine-learning processor 20 may use the total amount of assetsbelonging to that class. Each class then has two more granularitylevels. For each sector in (CUSIP) code [S02] lookup table 355, themachine-learning processor 20 may use the total amount of assetsbelonging to that sector. Each sector then has two more granularitylevels.

In some embodiments, for each portfolio and for each segmentation, themachine-learning processor 20 may generate a histogram representing howthe portfolio may be composed in terms of the different categories ofthat specific segmentation type. In a normalization step 365 byfeature_type [P04], each histogram may be normalized independently usingL2-normalization. Each normalized histogram may represent a set offeatures. In a [P05] concatenation step 370, the set of features may befused together or concatenated so as to generate a common featuresvector. In a [P06] final normalization step 375, each feature vector maybe normalized independently through L2-normalization. The final resultof the feature extraction data pipeline 300 is to generate a portfoliofeatures dataset [O3] that may include the features of each portfoliothat may be used to train the model.

FIG. 6 is an exemplary histogram 400 illustrating a total amount ofportfolio assets 420 split into asset classes 410 versus each assetclass 430 in accordance with one or more embodiments of the presentdisclosure.

FIG. 7 is an exemplary histogram 500 illustrating a total amount ofportfolio assets 520 split into sectors 520 versus each sector 530 inaccordance with one or more embodiments of the present disclosure.

In some embodiments, there may be a correlation between the probabilityof moving the credit line and the portfolio composition of the borrowerthat is opening a new loan which may be incorporated into the machinelearning model 24. Thus, the machine learning model 24 may be trained byusing as training data, the portfolio corresponding to each loan. Sincethese parameters may change as a function of time, the machine-learningprocessor 20 may assess this correlation using the machine learningmodel 24 at the time at which the new credit line is opened.

FIG. 8 is a flow diagram 600 for classifying assets based on a number ofattributes in accordance with one or more embodiments of the presentdisclosure. The machine-learning processor 20 may receive collateralholdings 615 of a user portfolio 65 and may [O1] sanitize the collateralholding in a step 605 to retrieve Assets_ID 610, or asset-basedidentifiers. The machine-learning processor 20 may further extract an L0Asset_Id in a step 620 and an L0 classification in a step 630.

In some embodiments, the machine-learning processor 20 may use an [S01]asset className lookup table and the data from the steps 620 and 630 toget the asset classification in a step 640 of the user portfolio 65. Theassets in the user portfolio 65 may be classified into an L1 asset class624, an L2 asset class 644 and/or an L3 asset class 646. The L1 class624 may include the asset main categories such as cash, equities, andfunds, etc. The L2 class 644 may include asset subcategories such asFunds-ETFs, Fund-Bonds, Mutual-Funds, Cash, etc. The L3 class 646 mayinclude Asset Sub-subcategories such as Cash-Calue Life Insurance,Cash-cash, Equity-Common shares, Equity-Convertible shares, Fund-USEquity Small Blend ETFs, etc.

In some embodiments, the machine-learning processor 20 may use an [S02]CUSIP lookup table 645 and the data from the steps 620 and 630 to getthe asset sectors in a step 650 of the user portfolio 65. The assets inthe user portfolio 65 may be classified into an L4 Sector 652, an L5Sub-sector 654 and/or an L5 country 656. The L4 Sector 652 may includethe asset main sectors such as Industrials, Consumer Staples, Utilities,etc. The L5 Sub-sector 654 may include asset subsectors such as theAerospace industry, Airline, Agriculture, etc. The L5 country 656 mayinclude USA, Brazil, Germany, etc.

FIG. 9 is a flow diagram 700 of a label creation data pipeline inaccordance with one or more embodiments of the present disclosure. The[L00] label creation data pipeline may be used to generate utilizationlabels for use in training the machine learning model 24. In a firststep in the label creation data pipeline may include detecting movementsin the user portfolio 65. The machine-learning processor 20 may [L01]detect a number of movements such as drawdowns/paybacks in a step 710from the user portfolio 65 for each outstanding balances time-series(TS) dataset 715 over a time interval 705 defined by Labels_start_dateto Labels_end_date. The machine-learning processor 20 may detect themovements by computing a first order derivative of a given times-series.The machine-learning processor 20 may count the number of movements thatmay have occurred in the first 365 days of each time series [L02], forexample.

In some embodiments, in a second step in the label creation datapipeline may include labels creation. The machine-learning processor 20may [L03] generate a utilization label for each loan in the userportfolio 65 in a step 725 according to the number of movementsoccurring in the outstanding balance within the first 365 days, forexample. If this number is greater than 0, then the label is 1,otherwise the label is 0. In a step 730, a [O4] Utilization Label isgenerated for each loan in the user portfolio 65 which may be added tothe portfolio features vector for training the machine learning model24.

In some embodiments, the time-series extraction pipeline 22 or afirst-type data pipeline may include a label creation functionality asdescribed in FIG. 9 to generate labeled loan data also referred to asuser-specific activity engagement labeling data.

FIG. 10 is a flow diagram 800 of a training data pipeline in accordancewith one or more embodiments of the present disclosure. The trainingdata pipeline may be used to create a model that is able to predict theutilization likelihood of a loan. In a step 805 denoted [S1] Pull Data,the machine-learning processor 20 may update the content of historicalfinancial data for the plurality of users by locally pulling data viathe Fastnet API. The machine-learning processor 20 may then execute twoparallel processes: a label creation process and a features extractionprocess.

In some embodiments, the machine-learning processor 20 may build a [S2]time series of the outstanding balance of each loan [S2] in a step 810.According to the pattern of each time series, the machine-learningprocessor 20 may create a label [S3] in a step 820. In parallel in astep 830, the machine-learning processor 20 may create features fromfinancial data of each portfolio [S4]. Once both features and labels foreach loan have been generated, the machine-learning processor 20 maystart training [S5] in a step 825 the machine learning model 24 to learnthe relationship between the features and labels.

FIG. 11 is a flow diagram 900 of an incremental learning inference datapipeline in accordance with one or more embodiments of the presentdisclosure. The incremental learning inference data pipeline (e.g., athird type of data pipeline) describes how the machine learning model 24learns from the training dataset. Suppose that there is a [M02] trainedmachine learning model 515 deployed in a production environment.Periodically. the incremental learning inference data pipeline mayreceive [O3] new portfolio features 905 and may predict [IL01] theutilization likelihood for each of them as utilization scores 920.

In some embodiments, a user may perform a [IL02] manual review 925 ofthe processed portfolio to know if any of the predicted utilizationlabels were correct or not. If not correct, the predicted utilizationlabels that are incorrect may be adjusted in a step 930 and the modelretrained [IL03] in a step 935. In this manner, new portfolio featuredata [O3-O4] and local annotated data in step 940 may be used to retraina better ML model [IL03] in the step 935. At this point the new modelmay be deployed and the process restarts, but with an improvedutilization prediction model [M03] in a step 950.

In some embodiments, a Light Gradient Boosting (LightGBM) model may beused since it provides an interpretation of the output. In particular,once the model is trained, the model provides an understanding as toboth which features are the most discriminative ones for the wholetraining dataset and which features are the most important ones todetermine the output of each test sample at inference time.

In some embodiments, the LightGBM framework may support differentmachine learning algorithms including gradient boosted trees (GBT),gradient boosted decision trees (GBDT), gradient boosted regressiontrees (GBRT), gradient boosted machine (GBM), multiple additiveregression trees (MART) and random forest (RF). LightGBM may use theadvantages of Extreme Gradient Boosting (XGBoost), including sparseoptimization, parallel training, multiple loss functions,regularization, bagging, and early stopping. A major difference betweenthe LightGBM and XGBoost may be in the construction of trees. LightGBMdoes not provide a tree level-wise—row by row—as most otherimplementations do, but grows trees leaf-wise. It may choose the leafyielding the largest decrease in loss. LightGBM may not use asorted-based decision tree learning algorithm, which may search for thebest split point on sorted feature values, as XGBoost or otherimplementations do. Instead, LightGBM may implement a highly optimizedhistogram-based decision tree learning algorithm, which may yieldadvantages on both in terms of computational efficiency and memoryconsumption. The LightGBM algorithm may utilize two novel techniquescalled Gradient-Based One-Side Sampling (GOSS) and Exclusive FeatureBundling (EFB) which may allow the algorithm to run faster whilemaintaining a high level of accuracy. LightGBM may operate on Linux,Windows, and macOS platforms and may support C++, Python, R, and C#.

FIG. 12 is a first graph 1000 illustrating computational results inaccordance with one or more embodiments of the present disclosure. Thefirst graph 1000 illustrates the results for a first exemplary use caseof single lead scoring. The first graph 1000 shows the resultsconsidering all loans in the test dataset. This accounts for a scenarioin which there is a single lead and the system predicts how likely 1020the single lead is going to move 1050 the credit line in the first 365days from its hypothetical opening, not move the credit line 1040, or aweighted outcome 1060.

The first graph 1000 shows that for a given input lead (e.g.,prospective customer), the machine learning (ML) model 24 may predictthe likelihood that the lead will move the credit line during the firstyear after its opening. The first graph 1000 shows the performance ofthe ML model 24, in terms of precision, recall and f1 (as shown in agraph legend 1010), based on test set that include a set of loans to beused for training the ML model 24.

The first group may refer to the NOT Moving sample 1040, the secondgroup to the Moving sample 1050, while the weighted group 1060 refers tothe weighted average results between the two classes. For example, forthe Moving class 1060, the precision of the Moving class is about 63%,such that for every 100 leads that the ML model 24 predicted will movethe line of credit during the first year, 63 leads actually did. Therecall of the Moving class 1060 is about 63%, such that for every 100leads that will actually move the line of credit during the first year,the ML model 24 successfully detects 63 of them. The f1 is a trade-offmetric between precision and recall and that may be typically used tosummarize the overall performance of the ML model 24. Note that thePrecision, Recall and f1 metrics are also shown for the NOT moving 1040and Weighted Average 1060 classes.

FIG. 13 is a second graph 1100 illustrating computational results inaccordance with one or more embodiments of the present disclosure. Inthis case, the results may consider all loans in the test dataset butranked 1120 by the prediction confidence score 1110. This is arepresentation of the scenario in which a financial advisor has a listof leads and has to decide which one that the financial advisor shouldstart trying to convince to utilize the loan line of credit. The secondgraph 1100 shows predicted results 1125 and baseline results 1130.

The second graph 1100 describes the performances of the ML model 24 froma different perspective, that is to simulate the job of a financialadvisor in selecting leads. The financial advisor may have, for example,a list of 100 possible leads. Without the ML model 24, the financialadvisor may randomly choose the first possible lead to engage with. Insome embodiments, using the ML model 24, the prediction outcome manager26 may display to the financial advisor on the graphic user interfaceGUIP 92B, for example, a list of leads ranked by the probability ofdrawing down a loan.

The x-axis 1120 of the second graph 1100 may indicate the specificpercentage of the training test set whose samples have been ranked by adescending probability of opening a credit line. The y-axis of thesecond graph 1100 may indicate the precision in percentage of the MLmodel 24 on that percentage of training test set. In order for the MLprocessor 20 to generate this chart, the training test set sample wasranked according to the predicted probability of moving the line ofcredit (in descending order). For example, when x is 11%, this refers tothe first 11% of the test samples being considered. The correspondingprecision of the ML model 24 may be computed, which is about 69% for afinancial advisor using the ML model 24, while the precision may be onlyabout 61% when the financial advisor does not use the ML model 24 (e.g.,works alone).

FIG. 14 is a third graph 1200 illustrating computational results inaccordance with one or more embodiments of the present disclosure. Thethird graph 1200 illustrates a Total Portfolio Market Value 1210 versusa yield probability 1220 of generating revenue by users using a line ofcredit over the first 12 months of SBL life. A legend 1230 of the thirdgraph 1200 shows data points for Ultra High Net Worth Individuals(UHNWI) T4, High Net Worth Individuals (HNWI) T3, a retail customer T2,and a retail customer T1. Each point on the third graph 1200 may be asample lead in the test set. The x-axis 1220 may indicate theprobability that a given lead may fall into that line, such that thelead may move the line of credit during its first 12 months so as togenerate yield. The y-axis 1210 is the total market value of each lead.The Total market value may be split in 4 value-based categories 1230,each of which represents a particular client segment such as ultra-highnet worth individual (UHNWI), high net worth individual (HNWI), etc.

FIG. 15 is a graph 1300 illustrating a potential usage of a line ofcredit in accordance with one or more embodiments of the presentdisclosure. The graph 1300 plots a commitment amount 1310, such as howmuch potential LOC usage that a user will use from a given line ofcredit LOC, as a function of portfolio movements 1320 of the user. Thegraph 1300 indicates a labeling of users, such as a user of highpotential 1325 for using the LOC of $3 million or more, for example,even though the user exhibits no-draw-down activity of the user'sportfolio, a user of high value 1335 for using the LOC of $3 million ormore, for example, with the user exhibits high utilization and/orfrequent draw-down activity of the user's portfolio, a user of tablestakes 1330 for using the LOC of $3 million or less, for example, andwhere the user exhibits no-draw-down activity of the user's portfolio,and a user of lifestyle 1330 for using the LOC of $3 million or less,for example, and where the user exhibits high utilization and/orfrequent draw-down activity of the user's portfolio. The machinelearning model 24 is configured to capture these parametric trends. Afinancial advisor should target a user that exhibits high utilizationand/or frequent draw-down activity of the user's portfolio and may beable to handle a larger LOC. Not shown in the graph 1300 is where theuser is determined not eligible to be offered an SBL Loan. FIG. 15refers to a line of credit of $3 million, which is by way of example andnot by limitation of the embodiments disclosed herein. Any suitablevalue of a line of credit (LOC) may be given to the user based on thedisclosed methods.

FIG. 16 is an exemplary output 1500 on graphical user interface 92A or92B in accordance with one or more embodiments of the presentdisclosure. When the financial advisor 85A or 85B (e.g., theentity-user) on any of the P computing devices 90A or 90B via GUI1 92Aor GUIP 92B runs at least one user for the system 10 to determinewhether or not to grant the at the least one user an SBL LOC, thefinancial advisor may receive the an exemplary output 1500 in GUI 92A or92B as shown in FIG. 16 . The exemplary output 1500 may display the sameset of data attributes 140 for the at least one user (in this exemplarycase for Richard Bryce 145) as shown in FIG. 3 as well as an assessment1510 of an influence of market volatility and/or interest rates on theuser's portfolio value.

In some embodiments, the financial advisor run the algorithms for asingle user 80A or 80B such as during a face-to-face meeting, forexample, as shown in FIG. 1 . In other embodiments, the financialadvisor may run the algorithms on a plurality of users and receive aranked list of the plurality of users with a prediction utilizationscore for assessing whether each of the plurality of users may use theSBL line of credit.

In some embodiments, the exemplary output 1500 may include a financialadvisor marketing kit such system 10 may provide the financial advisorwith a personalized script 1520, a personalized e-mail 1530 and/or apersonalized video 1540 with suggested user-customer communication for aspecific user from the at least one user.

In some embodiments, the model (e.g., the machine learning model 24) maybe executed by the machine-learning processor 20 as either a microservice or an API. Regardless, the model will receive as an input, acomposition of the portfolio 65 a potential user (e.g., customer), whichwill generate user-specific features and predict how likely an SBLcredit line corresponding to the portfolio 65 will be moved or used inthe first 365 days from its opening.

In some embodiments, the machine-learning processor 20 may receive aplurality of portfolios of a plurality of users (e.g., client datagathering), determine SBL qualification for each of the plurality ofusers respectively based on each of their portfolios, use the model(e.g., the machine learning model 24) to determine projected utilizationand LTV ranking, and the provide actionable rankings on the GUI 92A and92B for the financial advisors.

In some embodiments, training the machine learning model 24 may includethe machine-learning processor 20 generating a dataset of input andoutput vectors of data from the plurality of user that may include themodel input data, data features, and output data as classified below inTables I, II, and III. During training, the input and output vectors maybe applied to the input and the output of the machine learning model 24to train the machine learning model 24.

In some embodiments, Table I is a list of exemplary model inputs asshown below.

TABLE I Exemplary Model Inputs Exemplary Model Inputs  Account Name Account Registration  Income  Net Worth  Employment Status  EmploymentTenure  Education  Investment Portfolio Details  Investment PortfolioTransactions  Previous Marketing Campaign Results  CRM interactions Cash Balance  Credit History  Homeowner Status  Referring Advisor Code Referring Financial Institution Existing Customer Y/N

In some embodiments, Table II is a list of exemplary data features asshown below.

TABLE II Exemplary Data Features Exemplary Data Features: Date  year:Year.  quarter: Quarter.  month: Month.  week_of_year: Week of year.Tenure  days_since_loan_started: Days since the loan started. months_since_loan_started: Months since the loan started. Outstandingbalance  outstanding_balance: Outstanding balance. days_with_positive_os_qty: Quantity of days with positive outstandingbalance  during the entire length of the loan. days_with_positive_os_ratio: Quantity of days with positive outstandingbalance  during the entire length of the loan over the quantity of dayssince the loan started.  days_since_os_zero_qty: Quantity of consecutivedays with the outstanding  balance been zero. Days_since_os_positive_qty: Quantity of consecutive days with theoutstanding  balance been positive Drawdowns  drawdowns_qty: Quantity ofdrawdowns.  drawdowns_amount_sum: Total amount of money drawdowned. days_until_first_drawdown: Quantity of days elapsed when the firstdrawdown  occurred.  first_drawdown_amount: Amount of money drawdownedin the first drawdown.  first_drawdown_commitment_ratio: Amount of moneydrawdowned in the first  drawdown over the commitment amount available.days_since_last_drawdown: How many days have elapsed since the lastdrawdown . . . Payments  payments_qty: Quantity of payments. payments_amount_sum: Total amount of money paid. drawdowns_amount_paid_ratio: Total amount of money drawdowned that was already paid. Alerts  Is_on_top_up_alert: Boolean value that indicatesif the loan has an open top up  alert.  Is_on_sell_out_alert: Booleanvalue that indicates if the loan has an open sell out  alert. Is_on_margin_alert: Boolean value that indicates if the loan has anopen margin  alert.  days_since_top_up_alert_opened_qty: Quantity ofdays elapsed with an open top  up alert. days_since_sell_out_alert_opened_qty: Quantity of days elapsed with anopen sell  out alert.  top_up_alerts_opened_qty: Quantity of top upalerts opened during the entire  length of the loan. sell_out_alerts_opened_qty: Quantity of sell out alerts opened duringthe entire  length of the loan.  margin_alerts_opened_qty: Quantity ofmargin alerts opened during the entire  length of the loan. top_up_alerts_days_qty: Quantity of days with an open top up alertduring the  entire length of the loan.  sell_out_alerts_days_qty:Quantity of days with an open sell out alert during the  entire lengthof the loan.  margin_alerts_days_qty: Quantity of days with an openmargin alert during the  entire length of the loan. Portfolio Percentage equity - percentage of portfolio in equities  Percentagefixed income - percentage of portfolio in fixed income  Percentage MF-percentage of portfolio in mutual funds  Percentage ETF - percentage ofportfolio in etfs  percentage alternatives - percentage of portfolio inalternatives  MV change 3 months - change in portfolio ($ value) overthe last 3 months  MV change 1 months- change in portfolio ($ value)over the last 1 months  MV change 6 months - change in portfolio ($value) over the last 6 months  Total MV - $ portfolio is worth  Advisorcode - code of the advisor  Advisor institution - institution of theadvisor  Custodian - custodian of the account

In some embodiments, Table III is a list of exemplary outputs as shownbelow.

TABLE III Exemplary Outputs Exemplary Outputs  Client Number  AccountNumber  Propensity to Utilize Loan  Propensity to Initiate Loan Propensity to Churn in 3 months  Propensity to Churn in 6 months Propensity to Churn in 9 months  Propensity to Churn in 12 months Propensity to migrate segments  Model factors expressed as datafeatures and % importance for each propensity measure

FIG. 17 is a flow diagram 1600 of a pull single lead scoring inaccordance with one or more embodiments of the present disclosure. In afirst step 1620, any of the P the computing device processors such asthe processor1 or the processorP 91A or 91B of a financial advisor viaGUI1 92A or GUIP 92B operating banking customer relationship management(CRM) software (e.g., user 80A or 80B) may request to themachine-learning processor 20 over the communication network 30 toreceive from the prediction outcome manager 26 a utilization prediction1630 for the single user. In step 2 via a microservice, themachine-learning processor 20 may receive user-specific financial datafrom databases and electronic resources which may be input to themachine learning model 24 to receive a utilization prediction 1630 of aSBL line of credit for the single user. The utilization prediction maybe outputted to the GUI1 92A or GUIP 92B for the financial advisor toview as in Step 3 1640. In this scenario, each of the computing devicesmay be a separate bank and the user-specific financial data may berelayed to the server 15 over the communication network 30 fordetermination of the utilization prediction score. In other embodiments,the processes shown in the flow diagram 1600 may be performed on theserver 15 with all of the user-specific financial data for the singleuser stored in the user specific data object database 60.

FIG. 18 is a flow diagram 1700 for scoring a batch process of leads inaccordance with one or more embodiments of the present disclosure. Insome embodiments, a bank portfolios database 1705 (e.g., such as any ofthe N user portfolios 65 or 75) may be stored in any of the P memories93A or 93B on any of the P computing devices 90A or 90B. In otherembodiments, the bank portfolios database 1705 may be stored in thememory 45 of the server 15. In a first step 1710, a plurality ofportfolios of a respective plurality of users may be input in a batchprocess 1745 to the machine learning model in a second 1720 that outputsa utilization prediction score 1725 for each of the plurality ofusers.in a third step 1730, the bank portfolios database 1705 may beupdated with the utilization prediction score 1725 for each of theplurality of users.

In some embodiments, the utilization prediction score 1725 for each ofthe plurality of users may be ranked and a ranked list may be displayedto a financial advisor on any of the P graphical user interfaces (e.g.,GUI1 92A, GUIP 92B in FIG. 1 ).

FIG. 19 is a is a flowchart of an exemplary method 1735 for modelinguser-specific, activity-specific engagement predicting scores inaccordance with one or more embodiments of the present disclosure. Themethod 1735 may be performed by the machine-learning processor 20.

The method 1735 may include receiving 1740 an instruction to model atleast one user-specific activity-specific engagement predicting scorefor at least one user from a plurality of users.

In some embodiments, the instruction, for example, may be an electronicrequest from a financial advisor using GUI1 92A or GUIP 92B over thecommunication network 30 for the server 15 to model at least oneuser-specific activity-specific engagement predicting score. The term“at least one user-specific activity-specific engagement predictingscore” is synonymous and equivalent to the prediction utilization scorethat is the likelihood that the at least one user will use a line ofcredit as described herein above. In other embodiments, the predictionutilization score may include the likelihood that the at least one userwill churn the line of credit with a higher score indicating a higherprobability of user churning, and a lower score indicate a lowerprobability of user churning. The computation and/or modeling of the atleast one user-specific activity-specific engagement predicting score isthe performed by the machine learning processor 20 by applying thetime-series extraction pipeline 22 and features extraction pipeline 23to the user-specific, activity specific data.

In some embodiments, a first algorithm may be used for computing a firstprediction utilization score that is the likelihood that the at leastone user will use a line of credit. Likewise, a second algorithm may beused for computing a second prediction utilization score that is thelikelihood that the at least one user will churn the line of credit. Thefirst and second algorithms may be separate, independent, and/ordecoupled from one another.

The user-specific, activity specific data may be financial data (e.g.,both assets and/or debts), and/or personal data (age, gender,demographic attributes, psychographic attributes, and/or behavioralattributes) obtained from any of the digital resources and/or provideddirectly from the at least one user. Note that the terms digitalresources and electronic resources may be used interchangeably hereinand examples are provided as described herein above.

The method 1735 may include obtaining 1745 from a plurality of digitalresources, based on the instruction, user-specific, activity-specificdata where the user-specific, activity-specific data includes (i) atleast one user-specific activity performance data regarding performanceof at least one activity by the at least one user, (ii) at least oneobject data for at least one object that allows the at least one user toperform the at least one activity, and (iii) at least one user-specificpersonal data of the at least one user.

In some embodiments, at least one user-specific activity performancedata regarding performance of at least one activity by the at least oneuser may be, for example, historical data regarding loans (types,balances) and/or lines of credit (types, balances) provided to the user.The performance of the at least one activity may refer to historicaldata regarding the number of movements, and/or churning of loans and/orlines of credit made by the user.

In some embodiments, at least one object data for at least one objectthat allows the at least one user to perform the at least one activitymay refer to the current and/or historical data of accounts and/orsecurities (e.g., balances, and/or transactions), for example, held inthe user portfolio 75 that allows the user (e.g., by providingcollateral for the user) to perform the at least one activity regardingmovements and/or churning of old and/or new lines of credit.

In some embodiments, the at least one object of the at least one usermay be the data object 70 unique to a particular Nth user from aplurality of users that stores data, such as in data records, forexample, such as user debts, loans, and/or line of credit stored inobligation-based data record, asset-based data records 75 (such as theNth user portfolio 75, and user-specific personal data (e.g., PII, age,gender, demographic attributes, psychographic attributes, and/orbehavioral attributes).

The method 1735 may include predicting 1750 a user-specific activityengagement labeling data for the at least one activity by utilizing afirst-type data pipeline on the at least one user-specific activityperformance data.

In some embodiments, the user-specific activity engagement labeling datamay the data from outstanding balances TS dataset 255 and the loanssummary 260 outputted from time-series extraction process data pipeline200 (e.g., the first-type data pipeline) which may be then labeled asdescribed in the label creation flow diagram 700. In other embodiments,the processes of the label creation flow diagram 700 may be integrateddirectly or may be a part of the first-type data pipeline.

The method 1735 may include predicting 1755 a plurality ofuser-specific, activity-specific data features by utilizing asecond-type data pipeline on the user-specific, activity-specific data.

In some embodiments, the user-specific, activity-specific data may beinputting into the feature extraction data pipeline 300 (e.g., thesecond-type data pipeline) which may output the plurality ofuser-specific, activity-specific data features as shown for example inTable II.

The method 1735 may include predicting 1760 based on at least onemachine-learning model, the at least one user-specific activity-specificengagement predicting score, by utilizing: (i) the user-specificactivity engagement labeling data for the at least one activity, and(ii) the plurality of user-specific, activity-specific data features.

The method 1735 may include instructing 1765 based on the at least oneuser-specific activity-specific engagement predicting score, at leastone computing device to present at least one user-specificactivity-related action instruction that predicts at least oneuser-specific activity-related action to be performed with at least oneuser.

In some embodiments, the at least one machine-learning model may modeland predict the at least one user-specific activity-specific engagementpredicting score and/or the at least one user-specific activity-relatedaction instruction that predicts at least one user-specificactivity-related action to be performed with at least one user (e.g., atleast one recommendation to the financial advisor as to how to cause aparticular user to agree to accept and/or use the line of credit togenerate revenue for the entity.)

The embodiments disclosed herein improve the overall computationalefficiency of the server 15 in contrast to a computing system thatprocesses terabytes of raw user-specific activity specific data for eachuser from a plurality of users to determine at least one user-specificactivity-related action instruction that predicts a user-specificactivity-related action to be performed with each user. Suchuser-specific activity-related actions may include but are not limitedto establishing a line of credit within a specific timeframe (e.g., thenext 12 months), drawing down a certain amount of funds from that lineof credit within a specific timeframe or paying back funds against thatline of credit within a specific timeframe, according to a specifictemporal pattern(s) or other data driven pattern(s).

The technical improvements result from the machine learning processor 20using the user-specific activity specific data to generate smallerdatasets of labeled data and/or data features, which is transformedusing the machine learning models to the user-specific activity-specificengagement predicting score for each user. The machine learningprocessor 20 may use the user-specific activity-specific engagementpredicting score to instruct any of the P computing devices 90A and 90Bto display a user-specific activity-related action to be performed witheach user.

Thus, the ordered combination of the data pipelines disclosed herein togenerate the smaller user-specific datasets, may be transformed by themachine learning processor 20 to output user-specific activity-specificengagement predicting score used for displaying a user-specificactivity-related action to be performed with each user. Theseembodiments provide a technical improvement by significantly improvingthe computing speed and computationally efficiency relative to a systemthat merely processes the raw user-specific activity specific data. Suchtechnical improvements enrich existing customer data with a layer ofbehavioral customer data. Using such behavioral data, multiple signalscan be predicted in order for example but not limited to maximizingbusiness metrics such as customer lifetime value, net margincontribution by customer and other key metrics.

In some embodiments, exemplary inventive, specially programmed computingsystems/platforms with associated devices are configured to operate inthe distributed network environment, communicating with one another overone or more suitable data communication networks (e.g., the Internet,satellite, etc.) and utilizing one or more suitable data communicationprotocols/modes such as, without limitation, IPX/SPX, X.25, AX.25,AppleTalk™, TCP/IP (e.g., HTTP), near-field wireless communication(NFC), RFID, Narrow Band Internet of Things (NBIOT), 3G, 4G, 5G, GSM,GPRS, WiFi, WiMax, CDMA, satellite, ZigBee, and other suitablecommunication modes. In some embodiments, the NFC can represent ashort-range wireless communications technology in which NFC-enableddevices are “swiped,” “bumped,” “tap” or otherwise moved in closeproximity to communicate. In some embodiments, the NFC could include aset of short-range wireless technologies, typically requiring a distanceof 10 cm or less. In some embodiments, the NFC may operate at 13.56 MHzon ISO/IEC 18000-3 air interface and at rates ranging from 106 kbit/s to424 kbit/s. In some embodiments, the NFC can involve an initiator and atarget; the initiator actively generates an RF field that can power apassive target. In some embodiments, this can enable NFC targets to takevery simple form factors such as tags, stickers, key fobs, or cards thatdo not require batteries. In some embodiments, the NFC's peer-to-peercommunication can be conducted when a plurality of NFC-enable devices(e.g., smartphones) within close proximity of each other.

The material disclosed herein may be implemented in software or firmwareor a combination of them or as instructions stored on a machine-readablemedium, which may be read and executed by one or more processors, suchas one or more machine learning processors 20. A machine-readable mediummay include any medium and/or mechanism for storing or transmittinginformation in a form readable by a machine (e.g., a computing device).For example, a machine-readable medium may include read only memory(ROM); random access memory (RAM); magnetic disk storage media; opticalstorage media; flash memory devices; electrical, optical, acoustical orother forms of propagated signals (e.g., carrier waves, infraredsignals, digital signals, etc.), and others.

As used herein, the terms “computer engine” and “engine” identify atleast one software component and/or a combination of at least onesoftware component and at least one hardware component which aredesigned/programmed/configured to manage/control other software and/orhardware components (such as the libraries, software development kits(SDKs), objects, etc.).

Examples of hardware elements may include processors, microprocessors,circuits, circuit elements (e.g., transistors, resistors, capacitors,inductors, and so forth), integrated circuits, application specificintegrated circuits (ASIC), programmable logic devices (PLD), digitalsignal processors (DSP), field programmable gate array (FPGA), logicgates, registers, semiconductor device, chips, microchips, chip sets,and so forth. In some embodiments, the one or more processors may beimplemented as a Complex Instruction Set Computer (CISC) or ReducedInstruction Set Computer (RISC) processors; x86 instruction setcompatible processors, multi-core, or any other microprocessor orcentral processing unit (CPU). In various implementations, the one ormore processors may be dual-core processor(s), dual-core mobileprocessor(s), and so forth.

Computer-related systems, computer systems, and systems, as used herein,include any combination of hardware and software. Examples of softwaremay include software components, operating system software, middleware,firmware, software modules, routines, subroutines, functions, methods,procedures, software interfaces, application program interfaces (API),instruction sets, computer code, computer code segments, words, values,symbols, or any combination thereof. Determining whether an embodimentis implemented using hardware elements and/or software elements may varyin accordance with any number of factors, such as desired computationalrate, power levels, heat tolerances, processing cycle budget, input datarates, output data rates, memory resources, data bus speeds and otherdesign or performance constraints.

One or more aspects of at least one embodiment may be implemented byrepresentative instructions stored on a machine-readable medium whichrepresents various logic within the processor, which when read by amachine causes the machine to fabricate logic to perform the techniquesdescribed herein. Such representations, known as “IP cores” may bestored on a tangible, machine readable medium and supplied to variouscustomers or manufacturing facilities to load into the fabricationmachines that make the logic or processor. Of note, various embodimentsdescribed herein may, of course, be implemented using any appropriatehardware and/or computing software languages (e.g., C++, Objective-C,Swift, Java, JavaScript, Python, Perl, QT, etc.).

In some embodiments, one or more of exemplary inventive computer-basedsystems/platforms, exemplary inventive computer-based devices, and/orexemplary inventive computer-based components of the present disclosuremay include or be incorporated, partially or entirely into at least onepersonal computer (PC), laptop computer, ultra-laptop computer, tablet,touch pad, portable computer, handheld computer, palmtop computer,personal digital assistant (PDA), cellular telephone, combinationcellular telephone/PDA, television, smart device (e.g., smart phone,smart tablet or smart television), mobile internet device (MID),messaging device, data communication device, and so forth.

As used herein, the term “server” should be understood to refer to aservice point which provides processing, database, and communicationfacilities. By way of example, and not limitation, the term “server” canrefer to a single, physical processor with associated communications anddata storage and database facilities, or it can refer to a networked orclustered complex of processors and associated network and storagedevices, as well as operating software and one or more database systemsand application software that support the services provided by theserver. Cloud servers are examples.

In some embodiments, as detailed herein, one or more of exemplaryinventive computer-based systems/platforms, exemplary inventivecomputer-based devices, and/or exemplary inventive computer-basedcomponents of the present disclosure may obtain, manipulate, transfer,store, transform, generate, and/or output any digital object and/or dataunit (e.g., from inside and/or outside of a particular application) thatcan be in any suitable form such as, without limitation, a file, acontact, a task, an email, a social media post, a map, an entireapplication (e.g., a calculator), etc. In some embodiments, as detailedherein, one or more of exemplary inventive computer-basedsystems/platforms, exemplary inventive computer-based devices, and/orexemplary inventive computer-based components of the present disclosuremay be implemented across one or more of various computer platforms suchas, but not limited to: (1) FreeBSD, NetBSD, OpenBSD; (2) Linux; (3)Microsoft Windows; (4) OS X (MacOS); (5) MacOS 11; (6) Solaris; (7)Android; (8) iOS; (9) Embedded Linux; (10) Tizen; (11) WebOS; (12) IBMi; (13) IBM AIX; (14) Binary Runtime Environment for Wireless (BREW);(15) Cocoa (API); (16) Cocoa Touch; (17) Java Platforms; (18) JavaFX;(19) JavaFX Mobile; (20) Microsoft DirectX; (21) .NET Framework; (22)Silverlight; (23) Open Web Platform; (24) Oracle Database; (25) Qt; (26)Eclipse Rich Client Platform; (27) SAP NetWeaver; (28) Smartface; and/or(29) Windows Runtime.

In some embodiments, exemplary inventive computer-basedsystems/platforms, exemplary inventive computer-based devices, and/orexemplary inventive computer-based components of the present disclosuremay be configured to utilize hardwired circuitry that may be used inplace of or in combination with software instructions to implementfeatures consistent with principles of the disclosure. Thus,implementations consistent with principles of the disclosure are notlimited to any specific combination of hardware circuitry and software.For example, various embodiments may be embodied in many different waysas a software component such as, without limitation, a stand-alonesoftware package, a combination of software packages, or it may be asoftware package incorporated as a “tool” in a larger software product.

For example, exemplary software specifically programmed in accordancewith one or more principles of the present disclosure may bedownloadable from a network, for example, a website, as a stand-aloneproduct or as an add-in package for installation in an existing softwareapplication. For example, exemplary software specifically programmed inaccordance with one or more principles of the present disclosure mayalso be available as a client-server software application, or as aweb-enabled software application. For example, exemplary softwarespecifically programmed in accordance with one or more principles of thepresent disclosure may also be embodied as a software package installedon a hardware device.

In some embodiments, exemplary inventive computer-basedsystems/platforms, exemplary inventive computer-based devices, and/orexemplary inventive computer-based components of the present disclosuremay be configured to handle numerous concurrent users that may be, butis not limited to, at least 100 (e.g., but not limited to, 100-999), atleast 1,000 (e.g., but not limited to, 1,000-9,999), at least 10,000(e.g., but not limited to, 10,000-99,999), at least 100,000 (e.g., butnot limited to, 100,000-999,999), at least 1,000,000 (e.g., but notlimited to, 1,000,000-9,999,999), at least 10,000,000 (e.g., but notlimited to, 10,000,000-99,999,999), at least 100,000,000 (e.g., but notlimited to, 100,000,000-999,999,999), at least 1,000,000,000 (e.g., butnot limited to, 1,000,000,000-999,999,999,999), and so on.

In some embodiments, exemplary inventive computer-basedsystems/platforms, exemplary inventive computer-based devices, and/orexemplary inventive computer-based components of the present disclosuremay be configured to output to distinct, specifically programmedgraphical user interface implementations of the present disclosure(e.g., a desktop, a web app., etc.). In various implementations of thepresent disclosure, a final output may be displayed on a displayingscreen which may be, without limitation, a screen of a computer, ascreen of a mobile device, or the like. In various implementations, thedisplay may be a holographic display. In various implementations, thedisplay may be a transparent surface that may receive a visualprojection. Such projections may convey various forms of information,images, and/or objects. For example, such projections may be a visualoverlay for a mobile augmented reality (MAR) application.

In some embodiments, exemplary inventive computer-basedsystems/platforms, exemplary inventive computer-based devices, and/orexemplary inventive computer-based components of the present disclosuremay be configured to be utilized in various applications which mayinclude, but not limited to, gaming, mobile-device games, video chats,video conferences, live video streaming, video streaming and/oraugmented reality applications, mobile-device messenger applications,and others similarly suitable computer-device applications.

As used herein, the term “mobile electronic device,” or the like, mayrefer to any portable electronic device that may or may not be enabledwith location tracking functionality (e.g., MAC address, InternetProtocol (IP) address, or the like). For example, a mobile electronicdevice can include, but is not limited to, a mobile phone, PersonalDigital Assistant (PDA), Blackberry™, Pager, Smartphone, or any otherreasonable mobile electronic device.

As used herein, the terms “proximity detection,” “locating,” “locationdata,” “location information,” and “location tracking” refer to any formof location tracking technology or locating method that can be used toprovide a location of, for example, a particular computingdevice/system/platform of the present disclosure and/or any associatedcomputing devices, based at least in part on one or more of thefollowing techniques/devices, without limitation: accelerometer(s),gyroscope(s), Global Positioning Systems (GPS); GPS accessed usingBluetooth™; GPS accessed using any reasonable form of wireless and/ornon-wireless communication; WiFi™ server location data; Bluetooth™ basedlocation data; triangulation such as, but not limited to, network basedtriangulation, WiFi™ server information based triangulation, Bluetooth™server information based triangulation; Cell Identification basedtriangulation, Enhanced Cell Identification based triangulation,Uplink-Time difference of arrival (U-TDOA) based triangulation, Time ofarrival (TOA) based triangulation, Angle of arrival (AOA) basedtriangulation; techniques and systems using a geographic coordinatesystem such as, but not limited to, longitudinal and latitudinal based,geodesic height based, Cartesian coordinates based; Radio FrequencyIdentification such as, but not limited to, Long range RFID, Short rangeRFID; using any form of RFID tag such as, but not limited to active RFIDtags, passive RFID tags, battery assisted passive RFID tags; or anyother reasonable way to determine location. For ease, at times the abovevariations are not listed or are only partially listed; this is in noway meant to be a limitation.

As used herein, the terms “cloud,” “Internet cloud,” “cloud computing,”“cloud architecture,” and similar terms correspond to at least one ofthe following: (1) a large number of computers connected through areal-time communication network (e.g., Internet); (2) providing theability to run a program or application on many connected computers(e.g., physical machines, virtual machines (VMs)) at the same time; (3)network-based services, which appear to be provided by real serverhardware, and are in fact served up by virtual hardware (e.g., virtualservers), simulated by software running on one or more real machines(e.g., allowing to be moved around and scaled up (or down) on the flywithout affecting the end user).

In some embodiments, the exemplary inventive computer-basedsystems/platforms, the exemplary inventive computer-based devices,and/or the exemplary inventive computer-based components of the presentdisclosure may be configured to securely store and/or transmit data byutilizing one or more of encryption techniques (e.g., private/public keypair, Triple Data Encryption Standard (3DES), block cipher algorithms(e.g., IDEA, RC2, RC5, CAST and Skipjack), cryptographic hash algorithms(e.g., MD5, RIPEMD-160, RTR0, SHA-1, SHA-2, Tiger (TTH), WHIRLPOOL,RNGs).

The aforementioned examples are, of course, illustrative and notrestrictive.

As used herein, the term “user” shall have a meaning of at least oneuser. In some embodiments, the terms “user”, “subscriber” “consumer” or“customer” should be understood to refer to a user of an application orapplications as described herein and/or a consumer of data supplied by adata provider. By way of example, and not limitation, the terms “user”or “subscriber” can refer to a person who receives data provided by thedata or service provider over the Internet in a browser session, or canrefer to an automated software application which receives the data andstores or processes the data.

As used herein, a “financial instrument” refers to an equity ownership,debt or credit, typically in relation to a corporate or governmentalentity, where the financial instrument is typically traded via one ormore financial trading venues. Some examples of “financial instruments”can include, but are not limited to, stocks, bonds, commodities, swaps,futures, and currency.

FIG. 20 depicts a block diagram of an exemplary computer-basedsystem/platform 1800 in accordance with one or more embodiments of thepresent disclosure. However, not all of these components may be requiredto practice one or more embodiments, and variations in the arrangementand type of the components may be made without departing from the spiritor scope of various embodiments of the present disclosure. In someembodiments, the exemplary inventive computing devices and/or theexemplary inventive computing components of the exemplary computer-basedsystem/platform 1800 may be configured to manage a large number ofmembers and/or concurrent transactions, as detailed herein. In someembodiments, the exemplary computer-based system/platform 1800 may bebased on a scalable computer and/or network architecture thatincorporates varies strategies for assessing the data, caching,searching, and/or database connection pooling. An example of thescalable architecture is an architecture that is capable of operatingmultiple servers.

In some embodiments, referring to FIG. 20 , members 1802-1804 (e.g.,clients) of the exemplary computer-based system/platform 1800 mayinclude virtually any computing device capable of receiving and sendinga message over a network (e.g., cloud network), such as network 1805, toand from another computing device, such as servers 1806 and 1807, eachother, and the like. In some embodiments, the member devices 1802-1804may be personal computers, multiprocessor systems, microprocessor-basedor programmable consumer electronics, network PCs, and the like. In someembodiments, one or more member devices within member devices 1802-1804may include computing devices that typically connect using a wirelesscommunications medium such as cell phones, smart phones, pagers, walkietalkies, radio frequency (RF) devices, infrared (IR) devices, CBs,integrated devices combining one or more of the preceding devices, orvirtually any mobile computing device, and the like. In someembodiments, one or more member devices within member devices 1802-1804may be devices that are capable of connecting using a wired or wirelesscommunication medium such as a PDA, POCKET PC, wearable computer, alaptop, tablet, desktop computer, a netbook, a video game device, apager, a smart phone, an ultra-mobile personal computer (UMPC), and/orany other device that is equipped to communicate over a wired and/orwireless communication medium (e.g., NFC, RFID, NBIOT, 3G, 4G, 5G, GSM,GPRS, WiFi, WiMax, CDMA, satellite, ZigBee, etc.). In some embodiments,one or more member devices within member devices 1802-1804 may includemay run one or more applications, such as Internet browsers, mobileapplications, voice calls, video games, videoconferencing, and email,among others. In some embodiments, one or more member devices withinmember devices 1802-1804 may be configured to receive and to send webpages, and the like. In some embodiments, an exemplary specificallyprogrammed browser application of the present disclosure may beconfigured to receive and display graphics, text, multimedia, and thelike, employing virtually any web based language, including, but notlimited to Standard Generalized Markup Language (SMGL), such asHyperText Markup Language (HTML), a wireless application protocol (WAP),a Handheld Device Markup Language (HDML), such as Wireless MarkupLanguage (WML), WMLScript, XML, JavaScript, and the like. In someembodiments, a member device within member devices 1802-1804 may bespecifically programmed by either Java, .Net, QT, C, C++ and/or othersuitable programming language. In some embodiments, one or more memberdevices within member devices 1802-1804 may be specifically programmedinclude or execute an application to perform a variety of possibletasks, such as, without limitation, messaging functionality, browsing,searching, playing, streaming or displaying various forms of content,including locally stored or uploaded messages, images and/or video,and/or games.

In some embodiments, the exemplary network 1805 may provide networkaccess, data transport and/or other services to any computing devicecoupled to it. In some embodiments, the exemplary network 405 mayinclude and implement at least one specialized network architecture thatmay be based at least in part on one or more standards set by, forexample, without limitation, Global System for Mobile communication(GSM) Association, the Internet Engineering Task Force (IETF), and theWorldwide Interoperability for Microwave Access (WiMAX) forum. In someembodiments, the exemplary network 405 may implement one or more of aGSM architecture, a General Packet Radio Service (GPRS) architecture, aUniversal Mobile Telecommunications System (UMTS) architecture, and anevolution of UMTS referred to as Long Term Evolution (LTE). In someembodiments, the exemplary network 405 may include and implement, as analternative or in conjunction with one or more of the above, a WiMAXarchitecture defined by the WiMAX forum. In some embodiments and,optionally, in combination of any embodiment described above or below,the exemplary network 1805 may also include, for instance, at least oneof a local area network (LAN), a wide area network (WAN), the Internet,a virtual LAN (VLAN), an enterprise LAN, a layer 3 virtual privatenetwork (VPN), an enterprise IP network, or any combination thereof. Insome embodiments and, optionally, in combination of any embodimentdescribed above or below, at least one computer network communicationover the exemplary network 1805 may be transmitted based at least inpart on one of more communication modes such as but not limited to: NFC,RFID, Narrow Band Internet of Things (NBIOT), ZigBee, 3G, 4G, 5G, GSM,GPRS, WiFi, WiMax, CDMA, satellite and any combination thereof. In someembodiments, the exemplary network 1805 may also include mass storage,such as network attached storage (NAS), a storage area network (SAN), acontent delivery network (CDN) or other forms of computer or machinereadable media.

In some embodiments, the exemplary server 1806 or the exemplary server1807 may be a web server (or a series of servers) running a networkoperating system, examples of which may include but are not limited toMicrosoft Windows Server, Novell NetWare, or Linux. In some embodiments,the exemplary server 1806 or the exemplary server 1807 may be used forand/or provide cloud and/or network computing. Although not shown inFIG. 20 , in some embodiments, the exemplary server 1806 or theexemplary server 1807 may have connections to external systems likeemail, SMS messaging, text messaging, ad content providers, etc. Any ofthe features of the exemplary server 1806 may be also implemented in theexemplary server 1807 and vice versa.

In some embodiments, one or more of the exemplary servers 1806 and 1807may be specifically programmed to perform, in non-limiting example, asauthentication servers, search servers, email servers, social networkingservices servers, SMS servers, IM servers, MMS servers, exchangeservers, photo-sharing services servers, advertisement providingservers, financial/banking-related services servers, travel servicesservers, or any similarly suitable service-base servers for users of themember computing devices 1801-1804.

In some embodiments and, optionally, in combination of any embodimentdescribed above or below, for example, one or more exemplary computingmember devices 1802-1804, the exemplary server 1806, and/or theexemplary server 1807 may include a specifically programmed softwaremodule that may be configured to send, process, and receive informationusing a scripting language, a remote procedure call, an email, a tweet,Short Message Service (SMS), Multimedia Message Service (MMS), instantmessaging (IM), internet relay chat (IRC), mIRC, Jabber, an applicationprogramming interface, Simple Object Access Protocol (SOAP) methods,Common Object Request Broker Architecture (CORBA), HTTP (HypertextTransfer Protocol), REST (Representational State Transfer), or anycombination thereof.

FIG. 21 depicts a block diagram of another exemplary computer-basedsystem/platform 500 in accordance with one or more embodiments of thepresent disclosure. However, not all of these components may be requiredto practice one or more embodiments, and variations in the arrangementand type of the components may be made without departing from the spiritor scope of various embodiments of the present disclosure. In someembodiments, the member computing devices 1902 a, 1902 b thru 1902 nshown each at least includes a computer-readable medium, such as arandom-access memory (RAM) 1908 coupled to a processor 1910 or FLASHmemory. In some embodiments, the processor 1910 may executecomputer-executable program instructions stored in memory 1908. In someembodiments, the processor 1910 may include a microprocessor, an ASIC,and/or a state machine. In some embodiments, the processor 1910 mayinclude, or may be in communication with, media, for examplecomputer-readable media, which stores instructions that, when executedby the processor 1910, may cause the processor 1910 to perform one ormore steps described herein. In some embodiments, examples ofcomputer-readable media may include, but are not limited to, anelectronic, optical, magnetic, or other storage or transmission devicecapable of providing a processor, such as the processor 1910 of client1902 a, with computer-readable instructions. In some embodiments, otherexamples of suitable media may include, but are not limited to, a floppydisk, CD-ROM, DVD, magnetic disk, memory chip, ROM, RAM, an ASIC, aconfigured processor, all optical media, all magnetic tape or othermagnetic media, or any other medium from which a computer processor canread instructions. Also, various other forms of computer-readable mediamay transmit or carry instructions to a computer, including a router,private or public network, or other transmission device or channel, bothwired and wireless. In some embodiments, the instructions may comprisecode from any computer-programming language, including, for example, C,C++, Visual Basic, Java, Python, Perl, JavaScript, and etc.

In some embodiments, member computing devices 1902 a through 1902 n mayalso comprise a number of external or internal devices such as a mouse,a CD-ROM, DVD, a physical or virtual keyboard, a display, a speaker, orother input or output devices. In some embodiments, examples of membercomputing devices 1902 a through 1902 n (e.g., clients) may be any typeof processor-based platforms that are connected to a network 1906 suchas, without limitation, personal computers, digital assistants, personaldigital assistants, smart phones, pagers, digital tablets, laptopcomputers, Internet appliances, and other processor-based devices. Insome embodiments, member computing devices 1902 a through 1902 n may bespecifically programmed with one or more application programs inaccordance with one or more principles/methodologies detailed herein. Insome embodiments, member computing devices 1902 a through 1902 n mayoperate on any operating system capable of supporting a browser orbrowser-enabled application, such as Microsoft™, Windows™, and/or Linux.In some embodiments, member computing devices 1902 a through 1902 nshown may include, for example, personal computers executing a browserapplication program such as Microsoft Corporation's Internet Explorer™,Apple Computer, Inc.'s Safari™, Mozilla Firefox, and/or Opera. In someembodiments, through the member computing client devices 1902 a through1902 n, users, 1912 a through 1912 n, may communicate over the exemplarynetwork 1906 with each other and/or with other systems and/or devicescoupled to the network 1906. As shown in FIG. 21 , exemplary serverdevices 1904 and 1913 may be also coupled to the network 1906. In someembodiments, one or more member computing devices 1902 a through 1902 nmay be mobile clients.

In some embodiments, at least one database of exemplary databases 1907and 1915 may be any type of database, including a database managed by adatabase management system (DBMS). In some embodiments, an exemplaryDBMS-managed database may be specifically programmed as an engine thatcontrols organization, storage, management, and/or retrieval of data inthe respective database. In some embodiments, the exemplary DBMS-manageddatabase may be specifically programmed to provide the ability to query,backup and replicate, enforce rules, provide security, compute, performchange and access logging, and/or automate optimization. In someembodiments, the exemplary DBMS-managed database may be chosen fromOracle database, IBM DB2, Adaptive Server Enterprise, FileMaker,Microsoft Access, Microsoft SQL Server, MySQL, PostgreSQL, and a NoSQLimplementation. In some embodiments, the exemplary DBMS-managed databasemay be specifically programmed to define each respective schema of eachdatabase in the exemplary DBMS, according to a particular database modelof the present disclosure which may include a hierarchical model,network model, relational model, object model, or some other suitableorganization that may result in one or more applicable data structuresthat may include fields, records, files, and/or objects. In someembodiments, the exemplary DBMS-managed database may be specificallyprogrammed to include metadata about the data that is stored.

In some embodiments, the exemplary inventive computer-basedsystems/platforms, the exemplary inventive computer-based devices,and/or the exemplary inventive computer-based components of the presentdisclosure may be specifically configured to operate in an cloudcomputing/architecture such as, but not limiting to: infrastructure aservice (IaaS), platform as a service (PaaS), and/or software as aservice (SaaS). FIGS. 22 and 23 illustrate schematics of exemplaryimplementations of the cloud computing/architecture(s) in which theexemplary inventive computer-based systems/platforms, the exemplaryinventive computer-based devices, and/or the exemplary inventivecomputer-based components of the present disclosure may be specificallyconfigured to operate.

In some embodiments, the exemplary inventive computer-basedsystems/platforms, the exemplary inventive computer-based devices,and/or the exemplary inventive computer-based components of the presentdisclosure may be configured to utilize one or more exemplary AI/machinelearning techniques chosen from, but not limited to, decision trees,boosting, support-vector machines, neural networks, nearest neighboralgorithms, Naive Bayes, bagging, random forests, and the like. In someembodiments and, optionally, in combination of any embodiment describedabove or below, an exemplary neutral network technique may be one of,without limitation, feedforward neural network, radial basis functionnetwork, recurrent neural network, convolutional network (e.g., U-net)or other suitable network. In some embodiments and, optionally, incombination of any embodiment described above or below, an exemplaryimplementation of Neural Network may be executed as follows:

i) Define Neural Network architecture/model,ii) Transfer the input data to the exemplary neural network model,iii) Train the exemplary model incrementally,iv) determine the accuracy for a specific number of timesteps,v) apply the exemplary trained model to process the newly-received inputdata,vi) optionally and in parallel, continue to train the exemplary trainedmodel with a predetermined periodicity.

In some embodiments and, optionally, in combination of any embodimentdescribed above or below, the exemplary trained neural network model mayspecify a neural network by at least a neural network topology, a seriesof activation functions, and connection weights. For example, thetopology of a neural network may include a configuration of nodes of theneural network and connections between such nodes. In some embodimentsand, optionally, in combination of any embodiment described above orbelow, the exemplary trained neural network model may also be specifiedto include other parameters, including but not limited to, biasvalues/functions and/or aggregation functions. For example, anactivation function of a node may be a step function, sine function,continuous or piecewise linear function, sigmoid function, hyperbolictangent function, or other type of mathematical function that representsa threshold at which the node is activated. In some embodiments and,optionally, in combination of any embodiment described above or below,the exemplary aggregation function may be a mathematical function thatcombines (e.g., sum, product, etc.) input signals to the node. In someembodiments and, optionally, in combination of any embodiment describedabove or below, an output of the exemplary aggregation function may beused as input to the exemplary activation function. In some embodimentsand, optionally, in combination of any embodiment described above orbelow, the bias may be a constant value or function that may be used bythe aggregation function and/or the activation function to make the nodemore or less likely to be activated.

At least some aspects of the present disclosure will now be describedwith reference to the following numbered clauses.

-   -   1. A method may include:        -   receiving, by a machine-learning processor, an instruction            to model at least one user-specific activity-specific            engagement predicting score for at least one user from a            plurality of users;        -   obtaining, by the machine-learning processor, from a            plurality of digital resources, based on the instruction,            user-specific, activity-specific data;            -   where the user-specific, activity-specific data may                include:            -   (i) at least one user-specific activity performance data                regarding performance of at least one activity by the at                least one user,            -   (ii) at least one object data for at least one object                that allows the at least one user to perform the at                least one activity, and            -   (iii) at least one user-specific personal data of the at                least one user;        -   predicting, by the machine-learning processor, a            user-specific activity engagement labeling data for the at            least one activity by utilizing a first-type data pipeline            on the at least one user-specific activity performance data;        -   predicting, by the machine-learning processor, a plurality            of user-specific, activity-specific data features by            utilizing a second-type data pipeline on the user-specific,            activity-specific data;        -   predicting, by the machine-learning processor, based on at            least one machine-learning model, the at least one            user-specific activity-specific engagement predicting score,            by utilizing:            -   i) the user-specific activity engagement labeling data                for the at least one activity and            -   ii) the plurality of user-specific, activity-specific                data features; and        -   instructing, by the machine-learning processor, based on the            at least one user-specific activity-specific engagement            predicting score, at least one computing device to present            at least one user-specific activity-related action            instruction that predicts at least one user-specific            activity-related action to be performed with at least one            user.    -   2. The method according to clause 1, where the predicting the at        least one user-specific activity-specific engagement predicting        score may include outputting a prediction utilization score by        the at least one machine-learning model indicative of a        likelihood that the at least one user will use a line of credit.    -   3. The method according to clause 1, where the predicting the at        least one user-specific activity-specific engagement predicting        score may include outputting a prediction utilization score by        the at least one machine-learning model indicative of a        likelihood that the at least one user will churn a line of        credit after given to the at least one user. (In other        embodiments, the prediction utilization score may be indicative        of a likelihood that the churning occurs within a predefined        time interval.)    -   4. The method according to clause 1, where the predicting the        user-specific activity engagement labeling data by utilizing the        first-type data pipeline may include using a times-series data        pipeline to identify and to label at least one loan, at least        one line of credit or any combination thereof used by the at        least one user.    -   5. The method according to clause 1, where the predicting of the        plurality of user-specific, activity-specific data features may        include using a feature data pipeline on the user-specific,        activity-specific data with the user-specific, activity-specific        data.    -   6. The method according to clause 1, where the obtaining of the        user-specific, activity-specific data with the at least one        user-specific activity performance data regarding the        performance of the at least one activity by the at least one        user may include obtaining a loan data, a line of credit data,        or both respectively of a loan, a line of credit, or both, that        the at least one user used, churned, or both.    -   7. The method according to clause 1, where the obtaining of the        user-specific, activity-specific data with the at least one        object data for the at least one object that allows the at least        one user to perform the at least one activity may include        obtaining at least one asset data for at least one asset that        provides collateral for the at least one user to obtain a loan,        a line of credit, or both.    -   8. The method according to clause 1, where the at least one user        is a single user, and where the instructing the at least one        computing device may include instructing the at least one        computing device to display a prediction utilization score        indicative of a likelihood that the single user will use a line        of credit.    -   9. The method according to clause 1, where the at least one user        is a single user, and where the instructing the at least one        computing device may include instructing the at least one        computing device to display a prediction utilization score        indicative of a likelihood that the at least one user will churn        a line of credit after given to the at least one user.    -   10. The method according to clause 1, where the at least one        user is a set of users from the plurality of users, and where        the instructing the at least one computing device includes        instructing the at least one computing device to display a        prediction utilization score for each user in the set.    -   11. The method according to clause 10, further including        ranking, by the machine-learning processor, the prediction        utilization score for each user in the set, and displaying, by        the machine-learning processor, a ranking of the users based on        the ranked prediction utilization score for each user in the        set.    -   12. The method according to clause 11, where the instructing the        at least one computing device may include displaying        recommendations for convincing the ranked users in the set to        apply for a loan, a line of credit, or both.    -   13. A system may include a memory and a machine learning        processor. The machine-learning processor may execute computer        code stored in the memory that causes the machine-learning        processor to:        -   receive an instruction to model at least one user-specific            activity-specific engagement predicting score for at least            one user from a plurality of users;        -   obtain from a plurality of digital resources, based on the            instruction, user-specific, activity-specific data;            -   where the user-specific, activity-specific data may                include:            -   (i) at least one user-specific activity performance data                regarding performance of at least one activity by the at                least one user,            -   (ii) at least one object data for at least one object                that allows the at least one user to perform the at                least one activity, and            -   (iii) at least one user-specific personal data of the at                least one user;        -   predict a user-specific activity engagement labeling data            for the at least one activity by utilizing a first-type data            pipeline on the at least one user-specific activity            performance data;        -   predict a plurality of user-specific, activity-specific data            features by utilizing a second-type data pipeline on the            user-specific, activity-specific data;        -   predict based on at least one machine-learning model, the at            least one user-specific activity-specific engagement            predicting score, by utilizing:            -   i) the user-specific activity engagement labeling data                for the at least one activity and            -   ii) the plurality of user-specific, activity-specific                data features; and instruct based on the at least one                user-specific activity-specific engagement predicting                score, at least one computing device to present at least                one user-specific activity-related action instruction                that predicts at least one user-specific                activity-related action to be performed with at least                one user.    -   14. The system according to clause 13, where the        machine-learning processor is configured to predict the at least        one user-specific activity-specific engagement predicting score        by outputting a prediction utilization score by the at least one        machine-learning model indicative of a likelihood that the at        least one user will use a line of credit.    -   15. The system according to clause 13, where the        machine-learning processor is configured to predict the at least        one user-specific activity-specific engagement predicting score        by outputting a prediction utilization score by the at least one        machine-learning model indicative of a likelihood that the at        least one user will churn a line of credit after given to the at        least one user. (In other embodiments, the prediction        utilization score may be indicative of a likelihood that the        churning occurs within a predefined time interval.)    -   16. The system according to clause 13, where the        machine-learning processor is configured to predict the        user-specific activity engagement labeling data by utilizing the        first-type data pipeline by using a times-series data pipeline        to identify and to label at least one loan, at least one line of        credit or any combination thereof used by the at least one user.    -   17. The system according to clause 13, where the        machine-learning processor is configured to predict the        plurality of user-specific, activity-specific data features by        using a feature data pipeline on the user-specific,        activity-specific data with the user-specific, activity-specific        data.    -   18. The system according to clause 13, where the        machine-learning processor is configured to obtain the        user-specific, activity-specific data with the at least one        user-specific activity performance data regarding the        performance of the at least one activity by the at least one        user by obtaining a loan data, a line of credit data, or both        respectively of a loan, a line of credit, or both that the at        least one user used or churned.    -   19. The system according to clause 13, where the        machine-learning processor is configured to obtain of the        user-specific, activity-specific data with the at least one        object data for the at least one object that allows the at least        one user to perform the at least one activity by obtaining at        least one asset data for at least one asset that provides        collateral for the at least one user to obtain a loan, a line of        credit, or both. The system according to clause 13, where the at        least one user is a single user, and where the machine-learning        processor is configured to instruct the at least one computing        device by instructing the at least one computing device to        display a prediction utilization score indicative of a        likelihood that the single user will use a line of credit.    -   20. The system according to clause 13, where the at least one        user is a single user, and where the machine-learning processor        is configured to instruct the at least one computing device by        instructing the at least one computing device to display a        prediction utilization score indicative of a likelihood that the        at least one user will churn a line of credit after given to the        at least one user.    -   21. The system according to clause 13, where the at least one        user is a set of users from the plurality of users, and wherein        the machine-learning processor is configured to instruct the at        least one computing device by instructing the at least one        computing device to display a prediction utilization score for        each user in the set.    -   22. The system according to clause 22, where the        machine-learning processor is further configured to rank the        prediction utilization score for each user in the set, and to        display a ranking of the users based on the ranked prediction        utilization score for each user in the set.    -   23. The system according to clause 23, where the        machine-learning processor is configured to instruct the at        least one computing device by displaying recommendations for        convincing the ranked users in the set to apply for a loan, a        line of credit, or both.

Publications cited throughout this document are hereby incorporated byreference in their entirety. While one or more embodiments of thepresent disclosure have been described, it is understood that theseembodiments are illustrative only, and not restrictive, and that manymodifications may become apparent to those of ordinary skill in the art,including that various embodiments of the inventive methodologies, theinventive systems/platforms, and the inventive devices described hereincan be utilized in any combination with each other. Further still, thevarious steps may be carried out in any desired order (and any desiredsteps may be added and/or any desired steps may be eliminated).

1. A method, comprising: obtaining, by a processor, from a plurality ofdigital resources user-specific, activity-specific data for at least oneuser from a plurality of users; wherein the user-specific,activity-specific data comprises: (i) at least one user-specificactivity performance data regarding performance of at least one activityby the at least one user, (ii) at least one object data for at least oneobject that allows the at least one user to perform the at least oneactivity, and (iii) at least one user-specific personal data of the atleast one user; training, by the processor, a neural network machinelearning model to obtain a trained neural network machine learning modelthat is configured to predict: (i) a user-specific activity engagementlabeling data for the at least one activity based on a first-type datapipeline on the at least one user-specific activity performance data;(ii) a plurality of user-specific, activity-specific data features basedon a second-type data pipeline on the user-specific, activity-specificdata; (iii) at least one user-specific activity-specific engagementpredicting score, based on the user-specific activity engagementlabeling data for the at least one activity and the plurality ofuser-specific, activity-specific data features; wherein the at least oneuser-specific activity-specific engagement predicting score is based ona plurality of propensities to engage a plurality of utilization actionsassociated with the at least one activity related to a line of credit bythe at least one user of the plurality of users; wherein the training ofthe neural network machine learning model comprises: generating aplurality of datasets with a plurality of feature vectors associatedwith the user-specific activity-specific data for the plurality ofusers, inputting, into a neural network machine learning model, theplurality of datasets with the plurality of feature vectors associatedwith the user-specific activity-specific data for the plurality ofusers, and applying the plurality of feature vectors to an outputassociated with the neural network machine learning model; utilizing, bythe processor, the trained neural network machine learning model tooutput the at least one user-specific activity-specific engagementpredicting score based on the user-specific activity engagement labelingdata for the at least one activity and the plurality of user-specific,activity-specific data features; and instructing, by the processor,based on the at least one user-specific activity-specific engagementpredicting score, at least one computing device to present at least oneuser-specific activity-related action instruction that predicts at leastone user-specific activity-related action to be performed with at leastone user.